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Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action6h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI7h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability7h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents7h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning7h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI7h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes7h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies7h◆Universal Decision Learners7h◆Algorithmic Recourse of In-Context Learning for Tabular Data7h◆Graph Machine Learning in the Era of Large Language Models (LLMs)7h◆NGDBench: Towards Neural Graph Data Management7h◆End-to-End Compression for Tabular Foundation Models7h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning7h◆A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models7h◆Memory by Design: Probabilistic Sequence Layers7h◆The Terminal Representation in Reinforcement Learning7h◆Counterfactual Trace Auditing of LLM Agent Skills7h◆IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment7h◆Residual Reservoir Memory Networks7h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action6h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI7h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability7h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents7h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning7h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI7h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes7h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies7h◆Universal Decision Learners7h◆Algorithmic Recourse of In-Context Learning for Tabular Data7h◆Graph Machine Learning in the Era of Large Language Models (LLMs)7h◆NGDBench: Towards Neural Graph Data Management7h◆End-to-End Compression for Tabular Foundation Models7h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning7h◆A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models7h◆Memory by Design: Probabilistic Sequence Layers7h◆The Terminal Representation in Reinforcement Learning7h◆Counterfactual Trace Auditing of LLM Agent Skills7h◆IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment7h◆Residual Reservoir Memory Networks7h◆
News/Data-driven Acceleration of MPC with Guarantees
arxiv
PublishedMay 21, 2026 at 4:00 AM
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Data-driven Acceleration of MPC with Guarantees

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arXiv:2511.13588v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data-driven framework to accelerate MPC by replacing online optimization with a nonparametric policy con

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